Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations126079
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 MiB
Average record size in memory273.5 B

Variable types

Categorical14
Numeric10
Text5

Alerts

Benefit per order is highly overall correlated with Order Item Total and 3 other fieldsHigh correlation
Category Name is highly overall correlated with Customer Id and 4 other fieldsHigh correlation
Customer Country is highly overall correlated with Customer StateHigh correlation
Customer Id is highly overall correlated with Category NameHigh correlation
Customer State is highly overall correlated with Customer CountryHigh correlation
Days for shipment (scheduled) is highly overall correlated with Days for shipping (real) and 1 other fieldsHigh correlation
Days for shipping (real) is highly overall correlated with Days for shipment (scheduled) and 3 other fieldsHigh correlation
Delivery Status is highly overall correlated with Days for shipping (real) and 2 other fieldsHigh correlation
Department Name is highly overall correlated with Category Name and 2 other fieldsHigh correlation
Late_delivery_risk is highly overall correlated with Days for shipping (real) and 1 other fieldsHigh correlation
Market is highly overall correlated with Order RegionHigh correlation
Order Item Product Price is highly overall correlated with Category Name and 2 other fieldsHigh correlation
Order Item Quantity is highly overall correlated with SalesHigh correlation
Order Item Total is highly overall correlated with Benefit per order and 3 other fieldsHigh correlation
Order Profit Per Order is highly overall correlated with Benefit per order and 3 other fieldsHigh correlation
Order Region is highly overall correlated with MarketHigh correlation
Order Status is highly overall correlated with Delivery Status and 1 other fieldsHigh correlation
Product Price is highly overall correlated with Category Name and 2 other fieldsHigh correlation
Sales is highly overall correlated with Benefit per order and 5 other fieldsHigh correlation
Sales per customer is highly overall correlated with Benefit per order and 3 other fieldsHigh correlation
Shipping Mode is highly overall correlated with Days for shipment (scheduled) and 1 other fieldsHigh correlation
Type is highly overall correlated with Order StatusHigh correlation
Days for shipping (real) has 3588 (2.8%) zerosZeros
Order Item Discount has 6978 (5.5%) zerosZeros

Reproduction

Analysis started2024-09-19 09:50:53.403610
Analysis finished2024-09-19 09:51:13.918477
Duration20.51 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Type
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
DEBIT
48513 
TRANSFER
34784 
PAYMENT
29131 
CASH
13651 

Length

Max length8
Median length7
Mean length6.1815052
Min length4

Characters and Unicode

Total characters779358
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAYMENT
2nd rowPAYMENT
3rd rowPAYMENT
4th rowPAYMENT
5th rowPAYMENT

Common Values

ValueCountFrequency (%)
DEBIT 48513
38.5%
TRANSFER 34784
27.6%
PAYMENT 29131
23.1%
CASH 13651
 
10.8%

Length

2024-09-19T15:21:13.984710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:14.101737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
debit 48513
38.5%
transfer 34784
27.6%
payment 29131
23.1%
cash 13651
 
10.8%

Most occurring characters

ValueCountFrequency (%)
E 112428
14.4%
T 112428
14.4%
A 77566
10.0%
R 69568
8.9%
N 63915
8.2%
D 48513
6.2%
B 48513
6.2%
I 48513
6.2%
S 48435
6.2%
F 34784
 
4.5%
Other values (5) 114695
14.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 779358
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 112428
14.4%
T 112428
14.4%
A 77566
10.0%
R 69568
8.9%
N 63915
8.2%
D 48513
6.2%
B 48513
6.2%
I 48513
6.2%
S 48435
6.2%
F 34784
 
4.5%
Other values (5) 114695
14.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 779358
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 112428
14.4%
T 112428
14.4%
A 77566
10.0%
R 69568
8.9%
N 63915
8.2%
D 48513
6.2%
B 48513
6.2%
I 48513
6.2%
S 48435
6.2%
F 34784
 
4.5%
Other values (5) 114695
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 779358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 112428
14.4%
T 112428
14.4%
A 77566
10.0%
R 69568
8.9%
N 63915
8.2%
D 48513
6.2%
B 48513
6.2%
I 48513
6.2%
S 48435
6.2%
F 34784
 
4.5%
Other values (5) 114695
14.7%

Days for shipping (real)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4977593
Minimum0
Maximum6
Zeros3588
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:14.186759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6251132
Coefficient of variation (CV)0.46461551
Kurtosis-1.0056934
Mean3.4977593
Median Absolute Deviation (MAD)1
Skewness0.081968234
Sum440994
Variance2.640993
MonotonicityNot monotonic
2024-09-19T15:21:14.261776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 39418
31.3%
3 20108
15.9%
6 20102
15.9%
4 19957
15.8%
5 19622
15.6%
0 3588
 
2.8%
1 3284
 
2.6%
ValueCountFrequency (%)
0 3588
 
2.8%
1 3284
 
2.6%
2 39418
31.3%
3 20108
15.9%
4 19957
15.8%
5 19622
15.6%
6 20102
15.9%
ValueCountFrequency (%)
6 20102
15.9%
5 19622
15.6%
4 19957
15.8%
3 20108
15.9%
2 39418
31.3%
1 3284
 
2.6%
0 3588
 
2.8%

Days for shipment (scheduled)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
4
75188 
2
24646 
1
19373 
0
 
6872

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters126079
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
4 75188
59.6%
2 24646
 
19.5%
1 19373
 
15.4%
0 6872
 
5.5%

Length

2024-09-19T15:21:14.354797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:14.458822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 75188
59.6%
2 24646
 
19.5%
1 19373
 
15.4%
0 6872
 
5.5%

Most occurring characters

ValueCountFrequency (%)
4 75188
59.6%
2 24646
 
19.5%
1 19373
 
15.4%
0 6872
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126079
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 75188
59.6%
2 24646
 
19.5%
1 19373
 
15.4%
0 6872
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 126079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 75188
59.6%
2 24646
 
19.5%
1 19373
 
15.4%
0 6872
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 75188
59.6%
2 24646
 
19.5%
1 19373
 
15.4%
0 6872
 
5.5%

Benefit per order
Real number (ℝ)

HIGH CORRELATION 

Distinct11882
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.608891
Minimum-56.060001
Maximum119.51
Zeros916
Zeros (%)0.7%
Negative14591
Negative (%)11.6%
Memory size6.0 MiB
2024-09-19T15:21:14.562849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-56.060001
5-th percentile-22.981
Q110.56
median29.120001
Q353.150002
95-th percentile88.190002
Maximum119.51
Range175.57
Interquartile range (IQR)42.590001

Descriptive statistics

Standard deviation32.194877
Coefficient of variation (CV)1.0185387
Kurtosis-0.03514385
Mean31.608891
Median Absolute Deviation (MAD)20.720001
Skewness0.10426268
Sum3985217.4
Variance1036.5101
MonotonicityNot monotonic
2024-09-19T15:21:14.680877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 916
 
0.7%
46.79999924 180
 
0.1%
63.70000076 169
 
0.1%
38.22000122 147
 
0.1%
14.39999962 146
 
0.1%
12 146
 
0.1%
18 145
 
0.1%
72 143
 
0.1%
62.40000153 137
 
0.1%
43.68000031 137
 
0.1%
Other values (11872) 123813
98.2%
ValueCountFrequency (%)
-56.06000137 1
 
< 0.1%
-56.02000046 8
< 0.1%
-56 1
 
< 0.1%
-55.99000168 2
 
< 0.1%
-55.97999954 1
 
< 0.1%
-55.97000122 1
 
< 0.1%
-55.95999908 1
 
< 0.1%
-55.93999863 1
 
< 0.1%
-55.91999817 1
 
< 0.1%
-55.90999985 1
 
< 0.1%
ValueCountFrequency (%)
119.5100021 9
< 0.1%
119.5 17
< 0.1%
119.2900009 1
 
< 0.1%
119.0599976 2
 
< 0.1%
119.0500031 1
 
< 0.1%
118.8300018 8
< 0.1%
118.8199997 2
 
< 0.1%
118.8000031 18
< 0.1%
118.7900009 4
 
< 0.1%
118.7799988 12
< 0.1%

Sales per customer
Real number (ℝ)

HIGH CORRELATION 

Distinct2391
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.40764
Minimum7.4899998
Maximum318.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:14.807908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.4899998
5-th percentile37.790001
Q188
median124.79
Q3179.98
95-th percentile242.5
Maximum318.5
Range311.01
Interquartile range (IQR)91.979996

Descriptive statistics

Standard deviation63.465626
Coefficient of variation (CV)0.47931997
Kurtosis-0.52885893
Mean132.40764
Median Absolute Deviation (MAD)46.18
Skewness0.26566341
Sum16693823
Variance4027.8857
MonotonicityNot monotonic
2024-09-19T15:21:14.923937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.9899979 1147
 
0.9%
109.1900024 1143
 
0.9%
107.8899994 1143
 
0.9%
120.8899994 1141
 
0.9%
97.48999786 1141
 
0.9%
113.0899963 1139
 
0.9%
123.4899979 1139
 
0.9%
127.3899994 1136
 
0.9%
106.5899963 1135
 
0.9%
118.2900009 1133
 
0.9%
Other values (2381) 114682
91.0%
ValueCountFrequency (%)
7.489999771 3
 
< 0.1%
7.989999771 3
 
< 0.1%
8.18999958 3
 
< 0.1%
8.289999962 3
 
< 0.1%
8.390000343 3
 
< 0.1%
8.470000267 15
< 0.1%
8.489999771 3
 
< 0.1%
8.659999847 29
< 0.1%
8.68999958 3
 
< 0.1%
8.789999962 3
 
< 0.1%
ValueCountFrequency (%)
318.5 2
< 0.1%
316.7600098 1
 
< 0.1%
315.25 3
< 0.1%
313.6000061 1
 
< 0.1%
313.5599976 1
 
< 0.1%
312 2
< 0.1%
310.3599854 1
 
< 0.1%
308.75 2
< 0.1%
307.2000122 1
 
< 0.1%
307.1600037 3
< 0.1%

Delivery Status
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Late delivery
69120 
Advance shipping
28982 
Shipping on time
22540 
Shipping canceled
 
5437

Length

Max length17
Median length13
Mean length14.398441
Min length13

Characters and Unicode

Total characters1815341
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLate delivery
2nd rowLate delivery
3rd rowShipping on time
4th rowLate delivery
5th rowLate delivery

Common Values

ValueCountFrequency (%)
Late delivery 69120
54.8%
Advance shipping 28982
23.0%
Shipping on time 22540
 
17.9%
Shipping canceled 5437
 
4.3%

Length

2024-09-19T15:21:15.055624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:15.175650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
late 69120
25.2%
delivery 69120
25.2%
shipping 56959
20.7%
advance 28982
10.6%
on 22540
 
8.2%
time 22540
 
8.2%
canceled 5437
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 269756
14.9%
i 205578
11.3%
148619
 
8.2%
n 113918
 
6.3%
p 113918
 
6.3%
d 103539
 
5.7%
a 103539
 
5.7%
v 98102
 
5.4%
t 91660
 
5.0%
l 74557
 
4.1%
Other values (11) 492155
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1540643
84.9%
Space Separator 148619
 
8.2%
Uppercase Letter 126079
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 269756
17.5%
i 205578
13.3%
n 113918
 
7.4%
p 113918
 
7.4%
d 103539
 
6.7%
a 103539
 
6.7%
v 98102
 
6.4%
t 91660
 
5.9%
l 74557
 
4.8%
y 69120
 
4.5%
Other values (7) 296956
19.3%
Uppercase Letter
ValueCountFrequency (%)
L 69120
54.8%
A 28982
23.0%
S 27977
22.2%
Space Separator
ValueCountFrequency (%)
148619
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1666722
91.8%
Common 148619
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 269756
16.2%
i 205578
12.3%
n 113918
 
6.8%
p 113918
 
6.8%
d 103539
 
6.2%
a 103539
 
6.2%
v 98102
 
5.9%
t 91660
 
5.5%
l 74557
 
4.5%
L 69120
 
4.1%
Other values (10) 423035
25.4%
Common
ValueCountFrequency (%)
148619
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 269756
14.9%
i 205578
11.3%
148619
 
8.2%
n 113918
 
6.3%
p 113918
 
6.3%
d 103539
 
5.7%
a 103539
 
5.7%
v 98102
 
5.4%
t 91660
 
5.0%
l 74557
 
4.1%
Other values (11) 492155
27.1%

Late_delivery_risk
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
1
69120 
0
56959 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters126079
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 69120
54.8%
0 56959
45.2%

Length

2024-09-19T15:21:15.273682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:15.369209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 69120
54.8%
0 56959
45.2%

Most occurring characters

ValueCountFrequency (%)
1 69120
54.8%
0 56959
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126079
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 69120
54.8%
0 56959
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common 126079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 69120
54.8%
0 56959
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 69120
54.8%
0 56959
45.2%

Category Name
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Cleats
21465 
Men's Footwear
20265 
Women's Apparel
19114 
Indoor/Outdoor Games
17483 
Water Sports
13977 
Other values (33)
33775 

Length

Max length20
Median length18
Mean length13.027364
Min length4

Characters and Unicode

Total characters1642477
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCleats
2nd rowWomen's Apparel
3rd rowShop By Sport
4th rowWomen's Apparel
5th rowWomen's Apparel

Common Values

ValueCountFrequency (%)
Cleats 21465
17.0%
Men's Footwear 20265
16.1%
Women's Apparel 19114
15.2%
Indoor/Outdoor Games 17483
13.9%
Water Sports 13977
11.1%
Shop By Sport 10169
8.1%
Cardio Equipment 6352
 
5.0%
Electronics 2916
 
2.3%
Accessories 1694
 
1.3%
Golf Balls 1434
 
1.1%
Other values (28) 11210
8.9%

Length

2024-09-19T15:21:15.467232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cleats 21465
 
9.1%
men's 20689
 
8.8%
apparel 20574
 
8.8%
footwear 20265
 
8.6%
women's 19838
 
8.4%
games 18297
 
7.8%
indoor/outdoor 17483
 
7.4%
water 13977
 
6.0%
sports 13977
 
6.0%
shop 10169
 
4.3%
Other values (44) 58138
24.8%

Most occurring characters

ValueCountFrequency (%)
o 186650
 
11.4%
e 154928
 
9.4%
r 127723
 
7.8%
s 112044
 
6.8%
t 109955
 
6.7%
109696
 
6.7%
a 106002
 
6.5%
p 82741
 
5.0%
n 71595
 
4.4%
l 57803
 
3.5%
Other values (39) 523340
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1217495
74.1%
Uppercase Letter 253410
 
15.4%
Space Separator 109696
 
6.7%
Other Punctuation 60945
 
3.7%
Dash Punctuation 931
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 186650
15.3%
e 154928
12.7%
r 127723
10.5%
s 112044
9.2%
t 109955
9.0%
a 106002
8.7%
p 82741
6.8%
n 71595
 
5.9%
l 57803
 
4.7%
m 44487
 
3.7%
Other values (14) 163567
13.4%
Uppercase Letter
ValueCountFrequency (%)
S 36329
14.3%
W 33815
13.3%
C 29516
11.6%
G 24149
9.5%
A 22900
9.0%
M 21321
8.4%
F 20549
8.1%
I 18414
7.3%
O 17483
6.9%
B 13152
 
5.2%
Other values (9) 15782
6.2%
Other Punctuation
ValueCountFrequency (%)
' 41787
68.6%
/ 17483
28.7%
& 1643
 
2.7%
! 32
 
0.1%
Space Separator
ValueCountFrequency (%)
109696
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 931
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1470905
89.6%
Common 171572
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 186650
12.7%
e 154928
 
10.5%
r 127723
 
8.7%
s 112044
 
7.6%
t 109955
 
7.5%
a 106002
 
7.2%
p 82741
 
5.6%
n 71595
 
4.9%
l 57803
 
3.9%
m 44487
 
3.0%
Other values (33) 416977
28.3%
Common
ValueCountFrequency (%)
109696
63.9%
' 41787
 
24.4%
/ 17483
 
10.2%
& 1643
 
1.0%
- 931
 
0.5%
! 32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1642477
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 186650
 
11.4%
e 154928
 
9.4%
r 127723
 
7.8%
s 112044
 
6.8%
t 109955
 
6.7%
109696
 
6.7%
a 106002
 
6.5%
p 82741
 
5.0%
n 71595
 
4.4%
l 57803
 
3.5%
Other values (39) 523340
31.9%
Distinct563
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:15.675961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length19
Mean length7.705859
Min length2

Characters and Unicode

Total characters971547
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBayamon
2nd rowCaguas
3rd rowCaguas
4th rowCaguas
5th rowCaguas
ValueCountFrequency (%)
caguas 46699
30.8%
san 3519
 
2.3%
chicago 2741
 
1.8%
brooklyn 2386
 
1.6%
los 2372
 
1.6%
angeles 2372
 
1.6%
new 2183
 
1.4%
york 1518
 
1.0%
beach 1484
 
1.0%
city 1174
 
0.8%
Other values (585) 84968
56.1%
2024-09-19T15:21:16.001744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 159817
16.4%
s 77961
 
8.0%
u 62497
 
6.4%
g 62299
 
6.4%
o 59675
 
6.1%
C 57770
 
5.9%
e 56510
 
5.8%
n 53103
 
5.5%
l 42585
 
4.4%
i 42215
 
4.3%
Other values (42) 297115
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 794793
81.8%
Uppercase Letter 151417
 
15.6%
Space Separator 25337
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 159817
20.1%
s 77961
9.8%
u 62497
 
7.9%
g 62299
 
7.8%
o 59675
 
7.5%
e 56510
 
7.1%
n 53103
 
6.7%
l 42585
 
5.4%
i 42215
 
5.3%
r 40055
 
5.0%
Other values (16) 138076
17.4%
Uppercase Letter
ValueCountFrequency (%)
C 57770
38.2%
B 9783
 
6.5%
S 9693
 
6.4%
L 9113
 
6.0%
A 7566
 
5.0%
P 7315
 
4.8%
M 6537
 
4.3%
H 6004
 
4.0%
D 4020
 
2.7%
N 3927
 
2.6%
Other values (15) 29689
19.6%
Space Separator
ValueCountFrequency (%)
25337
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 946210
97.4%
Common 25337
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 159817
16.9%
s 77961
 
8.2%
u 62497
 
6.6%
g 62299
 
6.6%
o 59675
 
6.3%
C 57770
 
6.1%
e 56510
 
6.0%
n 53103
 
5.6%
l 42585
 
4.5%
i 42215
 
4.5%
Other values (41) 271778
28.7%
Common
ValueCountFrequency (%)
25337
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 971547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 159817
16.4%
s 77961
 
8.0%
u 62497
 
6.4%
g 62299
 
6.4%
o 59675
 
6.1%
C 57770
 
5.9%
e 56510
 
5.8%
n 53103
 
5.5%
l 42585
 
4.4%
i 42215
 
4.3%
Other values (42) 297115
30.6%

Customer Country
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
EE. UU.
77594 
Puerto Rico
48485 

Length

Max length11
Median length7
Mean length8.5382419
Min length7

Characters and Unicode

Total characters1076493
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPuerto Rico
2nd rowPuerto Rico
3rd rowPuerto Rico
4th rowPuerto Rico
5th rowPuerto Rico

Common Values

ValueCountFrequency (%)
EE. UU. 77594
61.5%
Puerto Rico 48485
38.5%

Length

2024-09-19T15:21:16.120771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:16.227806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ee 77594
30.8%
uu 77594
30.8%
puerto 48485
19.2%
rico 48485
19.2%

Most occurring characters

ValueCountFrequency (%)
E 155188
14.4%
. 155188
14.4%
U 155188
14.4%
126079
11.7%
o 96970
9.0%
P 48485
 
4.5%
u 48485
 
4.5%
e 48485
 
4.5%
r 48485
 
4.5%
t 48485
 
4.5%
Other values (3) 145455
13.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 407346
37.8%
Lowercase Letter 387880
36.0%
Other Punctuation 155188
 
14.4%
Space Separator 126079
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 96970
25.0%
u 48485
12.5%
e 48485
12.5%
r 48485
12.5%
t 48485
12.5%
i 48485
12.5%
c 48485
12.5%
Uppercase Letter
ValueCountFrequency (%)
E 155188
38.1%
U 155188
38.1%
P 48485
 
11.9%
R 48485
 
11.9%
Other Punctuation
ValueCountFrequency (%)
. 155188
100.0%
Space Separator
ValueCountFrequency (%)
126079
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 795226
73.9%
Common 281267
 
26.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 155188
19.5%
U 155188
19.5%
o 96970
12.2%
P 48485
 
6.1%
u 48485
 
6.1%
e 48485
 
6.1%
r 48485
 
6.1%
t 48485
 
6.1%
R 48485
 
6.1%
i 48485
 
6.1%
Common
ValueCountFrequency (%)
. 155188
55.2%
126079
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1076493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 155188
14.4%
. 155188
14.4%
U 155188
14.4%
126079
11.7%
o 96970
9.0%
P 48485
 
4.5%
u 48485
 
4.5%
e 48485
 
4.5%
r 48485
 
4.5%
t 48485
 
4.5%
Other values (3) 145455
13.5%

Customer Id
Real number (ℝ)

HIGH CORRELATION 

Distinct16158
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6557.3053
Minimum2
Maximum20757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:16.325828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile640
Q13208
median6352
Q39633
95-th percentile12188
Maximum20757
Range20755
Interquartile range (IQR)6425

Descriptive statistics

Standard deviation4059.5834
Coefficient of variation (CV)0.61909324
Kurtosis0.15956483
Mean6557.3053
Median Absolute Deviation (MAD)3208
Skewness0.49465161
Sum8.267385 × 108
Variance16480217
MonotonicityNot monotonic
2024-09-19T15:21:16.437864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5004 37
 
< 0.1%
10591 36
 
< 0.1%
12284 35
 
< 0.1%
11048 34
 
< 0.1%
5654 33
 
< 0.1%
2970 33
 
< 0.1%
5648 32
 
< 0.1%
1588 32
 
< 0.1%
3708 32
 
< 0.1%
8015 32
 
< 0.1%
Other values (16148) 125743
99.7%
ValueCountFrequency (%)
2 7
< 0.1%
3 12
< 0.1%
4 11
< 0.1%
5 6
 
< 0.1%
6 11
< 0.1%
7 13
< 0.1%
8 15
< 0.1%
9 7
< 0.1%
10 7
< 0.1%
11 11
< 0.1%
ValueCountFrequency (%)
20757 1
< 0.1%
20756 1
< 0.1%
20752 1
< 0.1%
20751 1
< 0.1%
20750 1
< 0.1%
20749 1
< 0.1%
20748 1
< 0.1%
20747 1
< 0.1%
20746 1
< 0.1%
20744 1
< 0.1%

Customer Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Consumer
65316 
Corporate
38224 
Home Office
22539 

Length

Max length11
Median length8
Mean length8.8394816
Min length8

Characters and Unicode

Total characters1114473
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome Office
2nd rowHome Office
3rd rowHome Office
4th rowHome Office
5th rowHome Office

Common Values

ValueCountFrequency (%)
Consumer 65316
51.8%
Corporate 38224
30.3%
Home Office 22539
 
17.9%

Length

2024-09-19T15:21:16.550054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:16.661079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
consumer 65316
43.9%
corporate 38224
25.7%
home 22539
 
15.2%
office 22539
 
15.2%

Most occurring characters

ValueCountFrequency (%)
o 164303
14.7%
e 148618
13.3%
r 141764
12.7%
C 103540
9.3%
m 87855
7.9%
n 65316
 
5.9%
s 65316
 
5.9%
u 65316
 
5.9%
f 45078
 
4.0%
t 38224
 
3.4%
Other values (7) 189143
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 943316
84.6%
Uppercase Letter 148618
 
13.3%
Space Separator 22539
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 164303
17.4%
e 148618
15.8%
r 141764
15.0%
m 87855
9.3%
n 65316
 
6.9%
s 65316
 
6.9%
u 65316
 
6.9%
f 45078
 
4.8%
t 38224
 
4.1%
p 38224
 
4.1%
Other values (3) 83302
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 103540
69.7%
H 22539
 
15.2%
O 22539
 
15.2%
Space Separator
ValueCountFrequency (%)
22539
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1091934
98.0%
Common 22539
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 164303
15.0%
e 148618
13.6%
r 141764
13.0%
C 103540
9.5%
m 87855
8.0%
n 65316
 
6.0%
s 65316
 
6.0%
u 65316
 
6.0%
f 45078
 
4.1%
t 38224
 
3.5%
Other values (6) 166604
15.3%
Common
ValueCountFrequency (%)
22539
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1114473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 164303
14.7%
e 148618
13.3%
r 141764
12.7%
C 103540
9.3%
m 87855
7.9%
n 65316
 
5.9%
s 65316
 
5.9%
u 65316
 
5.9%
f 45078
 
4.0%
t 38224
 
3.4%
Other values (7) 189143
17.0%

Customer State
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
PR
48485 
CA
20403 
NY
7931 
TX
6350 
IL
5358 
Other values (40)
37552 

Length

Max length5
Median length2
Mean length2.0000238
Min length2

Characters and Unicode

Total characters252161
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPR
2nd rowPR
3rd rowPR
4th rowPR
5th rowPR

Common Values

ValueCountFrequency (%)
PR 48485
38.5%
CA 20403
16.2%
NY 7931
 
6.3%
TX 6350
 
5.0%
IL 5358
 
4.2%
FL 3841
 
3.0%
OH 2850
 
2.3%
PA 2702
 
2.1%
MI 2617
 
2.1%
NJ 2167
 
1.7%
Other values (35) 23375
18.5%

Length

2024-09-19T15:21:16.760103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pr 48485
38.5%
ca 20403
16.2%
ny 7931
 
6.3%
tx 6350
 
5.0%
il 5358
 
4.2%
fl 3841
 
3.0%
oh 2850
 
2.3%
pa 2702
 
2.1%
mi 2617
 
2.1%
nj 2167
 
1.7%
Other values (35) 23375
18.5%

Most occurring characters

ValueCountFrequency (%)
P 51187
20.3%
R 49931
19.8%
A 30865
12.2%
C 24794
9.8%
N 15267
 
6.1%
I 10157
 
4.0%
L 9868
 
3.9%
T 8947
 
3.5%
Y 8279
 
3.3%
M 7563
 
3.0%
Other values (18) 35303
14.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 252156
> 99.9%
Decimal Number 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 51187
20.3%
R 49931
19.8%
A 30865
12.2%
C 24794
9.8%
N 15267
 
6.1%
I 10157
 
4.0%
L 9868
 
3.9%
T 8947
 
3.5%
Y 8279
 
3.3%
M 7563
 
3.0%
Other values (14) 35298
14.0%
Decimal Number
ValueCountFrequency (%)
5 2
40.0%
9 1
20.0%
7 1
20.0%
8 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 252156
> 99.9%
Common 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 51187
20.3%
R 49931
19.8%
A 30865
12.2%
C 24794
9.8%
N 15267
 
6.1%
I 10157
 
4.0%
L 9868
 
3.9%
T 8947
 
3.5%
Y 8279
 
3.3%
M 7563
 
3.0%
Other values (14) 35298
14.0%
Common
ValueCountFrequency (%)
5 2
40.0%
9 1
20.0%
7 1
20.0%
8 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 51187
20.3%
R 49931
19.8%
A 30865
12.2%
C 24794
9.8%
N 15267
 
6.1%
I 10157
 
4.0%
L 9868
 
3.9%
T 8947
 
3.5%
Y 8279
 
3.3%
M 7563
 
3.0%
Other values (18) 35303
14.0%

Department Name
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Apparel
42712 
Fan Shop
32371 
Golf
30322 
Outdoors
8343 
Footwear
8095 
Other values (4)
 
4236

Length

Max length10
Median length9
Mean length6.7117284
Min length4

Characters and Unicode

Total characters846208
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApparel
2nd rowGolf
3rd rowGolf
4th rowGolf
5th rowGolf

Common Values

ValueCountFrequency (%)
Apparel 42712
33.9%
Fan Shop 32371
25.7%
Golf 30322
24.1%
Outdoors 8343
 
6.6%
Footwear 8095
 
6.4%
Fitness 1857
 
1.5%
Discs Shop 1517
 
1.2%
Pet Shop 463
 
0.4%
Book Shop 399
 
0.3%

Length

2024-09-19T15:21:16.864126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:16.991155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
apparel 42712
26.6%
shop 34750
21.6%
fan 32371
20.1%
golf 30322
18.9%
outdoors 8343
 
5.2%
footwear 8095
 
5.0%
fitness 1857
 
1.2%
discs 1517
 
0.9%
pet 463
 
0.3%
book 399
 
0.2%

Most occurring characters

ValueCountFrequency (%)
p 120174
14.2%
o 98746
11.7%
a 83178
9.8%
l 73034
 
8.6%
r 59150
 
7.0%
e 53127
 
6.3%
A 42712
 
5.0%
F 42323
 
5.0%
34750
 
4.1%
S 34750
 
4.1%
Other values (16) 204264
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 650629
76.9%
Uppercase Letter 160829
 
19.0%
Space Separator 34750
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 120174
18.5%
o 98746
15.2%
a 83178
12.8%
l 73034
11.2%
r 59150
9.1%
e 53127
8.2%
h 34750
 
5.3%
n 34228
 
5.3%
f 30322
 
4.7%
t 18758
 
2.9%
Other values (7) 45162
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
A 42712
26.6%
F 42323
26.3%
S 34750
21.6%
G 30322
18.9%
O 8343
 
5.2%
D 1517
 
0.9%
P 463
 
0.3%
B 399
 
0.2%
Space Separator
ValueCountFrequency (%)
34750
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 811458
95.9%
Common 34750
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 120174
14.8%
o 98746
12.2%
a 83178
10.3%
l 73034
9.0%
r 59150
 
7.3%
e 53127
 
6.5%
A 42712
 
5.3%
F 42323
 
5.2%
S 34750
 
4.3%
h 34750
 
4.3%
Other values (15) 169514
20.9%
Common
ValueCountFrequency (%)
34750
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 846208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 120174
14.2%
o 98746
11.7%
a 83178
9.8%
l 73034
 
8.6%
r 59150
 
7.0%
e 53127
 
6.3%
A 42712
 
5.0%
F 42323
 
5.0%
34750
 
4.1%
S 34750
 
4.1%
Other values (16) 204264
24.1%

Market
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
LATAM
36698 
Europe
34053 
Pacific Asia
28561 
USCA
18448 
Africa
8319 

Length

Max length12
Median length6
Mean length6.775482
Min length4

Characters and Unicode

Total characters854246
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPacific Asia
2nd rowPacific Asia
3rd rowPacific Asia
4th rowPacific Asia
5th rowPacific Asia

Common Values

ValueCountFrequency (%)
LATAM 36698
29.1%
Europe 34053
27.0%
Pacific Asia 28561
22.7%
USCA 18448
14.6%
Africa 8319
 
6.6%

Length

2024-09-19T15:21:17.112687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:17.231714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
latam 36698
23.7%
europe 34053
22.0%
pacific 28561
18.5%
asia 28561
18.5%
usca 18448
11.9%
africa 8319
 
5.4%

Most occurring characters

ValueCountFrequency (%)
A 128724
15.1%
i 94002
 
11.0%
a 65441
 
7.7%
c 65441
 
7.7%
r 42372
 
5.0%
f 36880
 
4.3%
L 36698
 
4.3%
T 36698
 
4.3%
M 36698
 
4.3%
E 34053
 
4.0%
Other values (10) 277239
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 468909
54.9%
Uppercase Letter 356776
41.8%
Space Separator 28561
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 94002
20.0%
a 65441
14.0%
c 65441
14.0%
r 42372
9.0%
f 36880
 
7.9%
u 34053
 
7.3%
o 34053
 
7.3%
p 34053
 
7.3%
e 34053
 
7.3%
s 28561
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
A 128724
36.1%
L 36698
 
10.3%
T 36698
 
10.3%
M 36698
 
10.3%
E 34053
 
9.5%
P 28561
 
8.0%
U 18448
 
5.2%
S 18448
 
5.2%
C 18448
 
5.2%
Space Separator
ValueCountFrequency (%)
28561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 825685
96.7%
Common 28561
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128724
15.6%
i 94002
 
11.4%
a 65441
 
7.9%
c 65441
 
7.9%
r 42372
 
5.1%
f 36880
 
4.5%
L 36698
 
4.4%
T 36698
 
4.4%
M 36698
 
4.4%
E 34053
 
4.1%
Other values (9) 248678
30.1%
Common
ValueCountFrequency (%)
28561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 854246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128724
15.1%
i 94002
 
11.0%
a 65441
 
7.7%
c 65441
 
7.7%
r 42372
 
5.0%
f 36880
 
4.3%
L 36698
 
4.3%
T 36698
 
4.3%
M 36698
 
4.3%
E 34053
 
4.0%
Other values (10) 277239
32.5%
Distinct3566
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:17.439951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length29
Mean length8.5641066
Min length2

Characters and Unicode

Total characters1079754
Distinct characters77
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)0.1%

Sample

1st rowMirzapur
2nd rowMurray Bridge
3rd rowKartal
4th rowUlan Bator
5th rowEstambul
ValueCountFrequency (%)
san 4614
 
2.9%
city 3943
 
2.5%
de 2288
 
1.4%
santo 1720
 
1.1%
los 1718
 
1.1%
domingo 1647
 
1.0%
new 1628
 
1.0%
york 1625
 
1.0%
angeles 1304
 
0.8%
tegucigalpa 1254
 
0.8%
Other values (3736) 137828
86.4%
2024-09-19T15:21:17.780961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 138925
 
12.9%
e 81275
 
7.5%
n 80197
 
7.4%
o 77851
 
7.2%
i 66005
 
6.1%
r 59661
 
5.5%
l 53670
 
5.0%
u 40386
 
3.7%
t 39440
 
3.7%
s 39099
 
3.6%
Other values (67) 403245
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 883770
81.8%
Uppercase Letter 157908
 
14.6%
Space Separator 33490
 
3.1%
Dash Punctuation 4098
 
0.4%
Other Punctuation 453
 
< 0.1%
Close Punctuation 15
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Final Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 138925
15.7%
e 81275
 
9.2%
n 80197
 
9.1%
o 77851
 
8.8%
i 66005
 
7.5%
r 59661
 
6.8%
l 53670
 
6.1%
u 40386
 
4.6%
t 39440
 
4.5%
s 39099
 
4.4%
Other values (30) 207261
23.5%
Uppercase Letter
ValueCountFrequency (%)
S 19722
12.5%
C 17941
11.4%
M 16408
 
10.4%
B 11586
 
7.3%
P 10668
 
6.8%
L 10341
 
6.5%
A 9455
 
6.0%
T 7160
 
4.5%
D 6321
 
4.0%
N 5555
 
3.5%
Other values (19) 42751
27.1%
Other Punctuation
ValueCountFrequency (%)
' 338
74.6%
? 103
 
22.7%
. 12
 
2.6%
Space Separator
ValueCountFrequency (%)
33490
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4098
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Final Punctuation
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1041678
96.5%
Common 38076
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 138925
 
13.3%
e 81275
 
7.8%
n 80197
 
7.7%
o 77851
 
7.5%
i 66005
 
6.3%
r 59661
 
5.7%
l 53670
 
5.2%
u 40386
 
3.9%
t 39440
 
3.8%
s 39099
 
3.8%
Other values (59) 365169
35.1%
Common
ValueCountFrequency (%)
33490
88.0%
- 4098
 
10.8%
' 338
 
0.9%
? 103
 
0.3%
) 15
 
< 0.1%
( 15
 
< 0.1%
. 12
 
< 0.1%
5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1068688
99.0%
None 11061
 
1.0%
Punctuation 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 138925
 
13.0%
e 81275
 
7.6%
n 80197
 
7.5%
o 77851
 
7.3%
i 66005
 
6.2%
r 59661
 
5.6%
l 53670
 
5.0%
u 40386
 
3.8%
t 39440
 
3.7%
s 39099
 
3.7%
Other values (49) 392179
36.7%
None
ValueCountFrequency (%)
á 2986
27.0%
í 2913
26.3%
ó 1568
14.2%
é 1038
 
9.4%
ã 957
 
8.7%
ú 806
 
7.3%
ç 216
 
2.0%
ü 184
 
1.7%
ñ 125
 
1.1%
Á 104
 
0.9%
Other values (7) 164
 
1.5%
Punctuation
ValueCountFrequency (%)
5
100.0%
Distinct164
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:17.984939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length31
Median length22
Mean length8.7930663
Min length4

Characters and Unicode

Total characters1108621
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowIndia
2nd rowAustralia
3rd rowTurquía
4th rowMongolia
5th rowTurquía
ValueCountFrequency (%)
unidos 17773
 
11.0%
estados 17751
 
11.0%
méxico 9398
 
5.8%
francia 8954
 
5.6%
alemania 6409
 
4.0%
australia 5860
 
3.6%
brasil 5674
 
3.5%
reino 4896
 
3.0%
unido 4896
 
3.0%
china 3939
 
2.4%
Other values (175) 75696
46.9%
2024-09-19T15:21:18.304733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 168375
15.2%
i 115881
 
10.5%
s 83352
 
7.5%
n 83164
 
7.5%
o 77373
 
7.0%
d 58751
 
5.3%
r 46568
 
4.2%
l 43158
 
3.9%
e 36939
 
3.3%
t 35702
 
3.2%
Other values (51) 359358
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 912265
82.3%
Uppercase Letter 160631
 
14.5%
Space Separator 35167
 
3.2%
Open Punctuation 270
 
< 0.1%
Close Punctuation 270
 
< 0.1%
Dash Punctuation 18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 168375
18.5%
i 115881
12.7%
s 83352
9.1%
n 83164
9.1%
o 77373
8.5%
d 58751
 
6.4%
r 46568
 
5.1%
l 43158
 
4.7%
e 36939
 
4.0%
t 35702
 
3.9%
Other values (22) 163002
17.9%
Uppercase Letter
ValueCountFrequency (%)
E 24111
15.0%
U 23624
14.7%
A 16814
10.5%
I 11789
7.3%
M 11633
7.2%
C 10739
 
6.7%
F 10592
 
6.6%
R 9547
 
5.9%
B 8638
 
5.4%
S 6109
 
3.8%
Other values (15) 27035
16.8%
Space Separator
ValueCountFrequency (%)
35167
100.0%
Open Punctuation
ValueCountFrequency (%)
( 270
100.0%
Close Punctuation
ValueCountFrequency (%)
) 270
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1072896
96.8%
Common 35725
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 168375
15.7%
i 115881
 
10.8%
s 83352
 
7.8%
n 83164
 
7.8%
o 77373
 
7.2%
d 58751
 
5.5%
r 46568
 
4.3%
l 43158
 
4.0%
e 36939
 
3.4%
t 35702
 
3.3%
Other values (47) 323633
30.2%
Common
ValueCountFrequency (%)
35167
98.4%
( 270
 
0.8%
) 270
 
0.8%
- 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1081458
97.5%
None 27163
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 168375
15.6%
i 115881
 
10.7%
s 83352
 
7.7%
n 83164
 
7.7%
o 77373
 
7.2%
d 58751
 
5.4%
r 46568
 
4.3%
l 43158
 
4.0%
e 36939
 
3.4%
t 35702
 
3.3%
Other values (44) 332195
30.7%
None
ValueCountFrequency (%)
é 10155
37.4%
í 5054
18.6%
á 4418
16.3%
ú 4351
16.0%
ñ 2612
 
9.6%
ó 551
 
2.0%
Á 22
 
0.1%

Order Item Discount
Real number (ℝ)

ZEROS 

Distinct767
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.781839
Minimum0
Maximum60
Zeros6978
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:18.428761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.4000001
median10.8
Q322.49
95-th percentile40
Maximum60
Range60
Interquartile range (IQR)18.09

Descriptive statistics

Standard deviation13.042924
Coefficient of variation (CV)0.88236136
Kurtosis0.5487285
Mean14.781839
Median Absolute Deviation (MAD)8.0200002
Skewness1.0635925
Sum1863679.5
Variance170.11786
MonotonicityNot monotonic
2024-09-19T15:21:18.546954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6978
 
5.5%
6 3412
 
2.7%
10 2993
 
2.4%
2 2632
 
2.1%
18 2451
 
1.9%
4 2324
 
1.8%
8 2315
 
1.8%
26 2248
 
1.8%
30 2111
 
1.7%
9 1955
 
1.6%
Other values (757) 96660
76.7%
ValueCountFrequency (%)
0 6978
5.5%
0.100000001 3
 
< 0.1%
0.109999999 15
 
< 0.1%
0.119999997 29
 
< 0.1%
0.150000006 7
 
< 0.1%
0.159999996 7
 
< 0.1%
0.180000007 3
 
< 0.1%
0.200000003 16
 
< 0.1%
0.219999999 6
 
< 0.1%
0.230000004 44
 
< 0.1%
ValueCountFrequency (%)
60 2
 
< 0.1%
59.99000168 638
0.5%
59.97000122 1
 
< 0.1%
59.40000153 1
 
< 0.1%
58.5 3
 
< 0.1%
57.59999847 1
 
< 0.1%
56.99000168 1
 
< 0.1%
56.24000168 6
 
< 0.1%
56 3
 
< 0.1%
55.25 3
 
< 0.1%

Order Item Product Price
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.329405
Minimum9.9899998
Maximum249.99001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:18.666982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.9899998
5-th percentile25
Q149.98
median59.990002
Q3129.99001
95-th percentile199.99001
Maximum249.99001
Range240.00001
Interquartile range (IQR)80.010006

Descriptive statistics

Standard deviation54.526666
Coefficient of variation (CV)0.65435084
Kurtosis-0.019828239
Mean83.329405
Median Absolute Deviation (MAD)20
Skewness1.1008913
Sum10506088
Variance2973.1573
MonotonicityNot monotonic
2024-09-19T15:21:18.790009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.99000168 21733
17.2%
129.9900055 20385
16.2%
50 19114
15.2%
49.97999954 17483
13.9%
199.9900055 14084
11.2%
39.99000168 10366
8.2%
99.98999786 6186
 
4.9%
24.98999977 2231
 
1.8%
31.98999977 1373
 
1.1%
30 861
 
0.7%
Other values (47) 12263
9.7%
ValueCountFrequency (%)
9.989999771 280
 
0.2%
11.28999996 271
 
0.2%
11.53999996 529
 
0.4%
14.98999977 576
 
0.5%
15.98999977 583
 
0.5%
17.98999977 293
 
0.2%
19.98999977 859
 
0.7%
21.98999977 283
 
0.2%
22 297
 
0.2%
24.98999977 2231
1.8%
ValueCountFrequency (%)
249.9900055 110
 
0.1%
215.8200073 598
 
0.5%
210.8500061 183
 
0.1%
209.9900055 56
 
< 0.1%
199.9900055 14084
11.2%
199 61
 
< 0.1%
189 57
 
< 0.1%
179.9900055 57
 
< 0.1%
169.9900055 59
 
< 0.1%
164.3800049 432
 
0.3%

Order Item Quantity
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
1
58993 
2
18819 
3
17969 
4
16019 
5
14279 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters126079
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 58993
46.8%
2 18819
 
14.9%
3 17969
 
14.3%
4 16019
 
12.7%
5 14279
 
11.3%

Length

2024-09-19T15:21:18.899034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:19.016061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 58993
46.8%
2 18819
 
14.9%
3 17969
 
14.3%
4 16019
 
12.7%
5 14279
 
11.3%

Most occurring characters

ValueCountFrequency (%)
1 58993
46.8%
2 18819
 
14.9%
3 17969
 
14.3%
4 16019
 
12.7%
5 14279
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126079
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 58993
46.8%
2 18819
 
14.9%
3 17969
 
14.3%
4 16019
 
12.7%
5 14279
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 126079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 58993
46.8%
2 18819
 
14.9%
3 17969
 
14.3%
4 16019
 
12.7%
5 14279
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 58993
46.8%
2 18819
 
14.9%
3 17969
 
14.3%
4 16019
 
12.7%
5 14279
 
11.3%

Sales
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.18932
Minimum9.9899998
Maximum325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:19.127589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.9899998
5-th percentile39.990002
Q199.989998
median129.99001
Q3199.99001
95-th percentile250
Maximum325
Range315.01
Interquartile range (IQR)100.00001

Descriptive statistics

Standard deviation69.237358
Coefficient of variation (CV)0.47039662
Kurtosis-0.62058731
Mean147.18932
Median Absolute Deviation (MAD)69.929993
Skewness0.19810168
Sum18557482
Variance4793.8117
MonotonicityNot monotonic
2024-09-19T15:21:19.239614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.9900055 20385
 
16.2%
199.9900055 14084
 
11.2%
59.99000168 4736
 
3.8%
179.9700012 4578
 
3.6%
119.9800034 4552
 
3.6%
239.9600067 4455
 
3.5%
50 4286
 
3.4%
49.97999954 4196
 
3.3%
99.95999908 4028
 
3.2%
100 3965
 
3.1%
Other values (151) 56814
45.1%
ValueCountFrequency (%)
9.989999771 56
 
< 0.1%
11.28999996 271
0.2%
11.53999996 529
0.4%
14.98999977 124
 
0.1%
15.98999977 118
 
0.1%
17.98999977 62
 
< 0.1%
19.97999954 54
 
< 0.1%
19.98999977 176
 
0.1%
21.98999977 51
 
< 0.1%
22 64
 
0.1%
ValueCountFrequency (%)
325 33
 
< 0.1%
320 9
 
< 0.1%
319.9599915 24
 
< 0.1%
300 10
 
< 0.1%
299.9700012 1704
1.4%
299.9500122 3412
2.7%
299.8500061 10
 
< 0.1%
297 9
 
< 0.1%
284.9700012 9
 
< 0.1%
280 43
 
< 0.1%

Order Item Total
Real number (ℝ)

HIGH CORRELATION 

Distinct2391
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.40764
Minimum7.4899998
Maximum318.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:19.360642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.4899998
5-th percentile37.790001
Q188
median124.79
Q3179.98
95-th percentile242.5
Maximum318.5
Range311.01
Interquartile range (IQR)91.979996

Descriptive statistics

Standard deviation63.465626
Coefficient of variation (CV)0.47931997
Kurtosis-0.52885893
Mean132.40764
Median Absolute Deviation (MAD)46.18
Skewness0.26566341
Sum16693823
Variance4027.8857
MonotonicityNot monotonic
2024-09-19T15:21:19.477231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.9899979 1147
 
0.9%
109.1900024 1143
 
0.9%
107.8899994 1143
 
0.9%
120.8899994 1141
 
0.9%
97.48999786 1141
 
0.9%
113.0899963 1139
 
0.9%
123.4899979 1139
 
0.9%
127.3899994 1136
 
0.9%
106.5899963 1135
 
0.9%
118.2900009 1133
 
0.9%
Other values (2381) 114682
91.0%
ValueCountFrequency (%)
7.489999771 3
 
< 0.1%
7.989999771 3
 
< 0.1%
8.18999958 3
 
< 0.1%
8.289999962 3
 
< 0.1%
8.390000343 3
 
< 0.1%
8.470000267 15
< 0.1%
8.489999771 3
 
< 0.1%
8.659999847 29
< 0.1%
8.68999958 3
 
< 0.1%
8.789999962 3
 
< 0.1%
ValueCountFrequency (%)
318.5 2
< 0.1%
316.7600098 1
 
< 0.1%
315.25 3
< 0.1%
313.6000061 1
 
< 0.1%
313.5599976 1
 
< 0.1%
312 2
< 0.1%
310.3599854 1
 
< 0.1%
308.75 2
< 0.1%
307.2000122 1
 
< 0.1%
307.1600037 3
< 0.1%

Order Profit Per Order
Real number (ℝ)

HIGH CORRELATION 

Distinct11882
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.608891
Minimum-56.060001
Maximum119.51
Zeros916
Zeros (%)0.7%
Negative14591
Negative (%)11.6%
Memory size6.0 MiB
2024-09-19T15:21:19.604260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-56.060001
5-th percentile-22.981
Q110.56
median29.120001
Q353.150002
95-th percentile88.190002
Maximum119.51
Range175.57
Interquartile range (IQR)42.590001

Descriptive statistics

Standard deviation32.194877
Coefficient of variation (CV)1.0185387
Kurtosis-0.03514385
Mean31.608891
Median Absolute Deviation (MAD)20.720001
Skewness0.10426268
Sum3985217.4
Variance1036.5101
MonotonicityNot monotonic
2024-09-19T15:21:19.721791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 916
 
0.7%
46.79999924 180
 
0.1%
63.70000076 169
 
0.1%
38.22000122 147
 
0.1%
14.39999962 146
 
0.1%
12 146
 
0.1%
18 145
 
0.1%
72 143
 
0.1%
62.40000153 137
 
0.1%
43.68000031 137
 
0.1%
Other values (11872) 123813
98.2%
ValueCountFrequency (%)
-56.06000137 1
 
< 0.1%
-56.02000046 8
< 0.1%
-56 1
 
< 0.1%
-55.99000168 2
 
< 0.1%
-55.97999954 1
 
< 0.1%
-55.97000122 1
 
< 0.1%
-55.95999908 1
 
< 0.1%
-55.93999863 1
 
< 0.1%
-55.91999817 1
 
< 0.1%
-55.90999985 1
 
< 0.1%
ValueCountFrequency (%)
119.5100021 9
< 0.1%
119.5 17
< 0.1%
119.2900009 1
 
< 0.1%
119.0599976 2
 
< 0.1%
119.0500031 1
 
< 0.1%
118.8300018 8
< 0.1%
118.8199997 2
 
< 0.1%
118.8000031 18
< 0.1%
118.7900009 4
 
< 0.1%
118.7799988 12
< 0.1%

Order Region
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Central America
20092 
Western Europe
18317 
South America
10672 
Oceania
7016 
Northern Europe
6552 
Other values (18)
63430 

Length

Max length15
Median length14
Mean length12.617042
Min length6

Characters and Unicode

Total characters1590744
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Asia
2nd rowOceania
3rd rowWest Asia
4th rowEastern Asia
5th rowWest Asia

Common Values

ValueCountFrequency (%)
Central America 20092
15.9%
Western Europe 18317
14.5%
South America 10672
 
8.5%
Oceania 7016
 
5.6%
Northern Europe 6552
 
5.2%
Southeast Asia 6527
 
5.2%
Southern Europe 6405
 
5.1%
Caribbean 5934
 
4.7%
West of USA 5715
 
4.5%
South Asia 5311
 
4.2%
Other values (13) 33538
26.6%

Length

2024-09-19T15:21:19.839817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
europe 34053
13.5%
america 30764
12.2%
central 21670
 
8.6%
asia 21545
 
8.5%
south 18903
 
7.5%
western 18317
 
7.3%
of 13540
 
5.4%
usa 13540
 
5.4%
west 12682
 
5.0%
africa 8319
 
3.3%
Other values (11) 58718
23.3%

Most occurring characters

ValueCountFrequency (%)
e 185242
 
11.6%
r 153725
 
9.7%
141738
 
8.9%
a 130783
 
8.2%
t 118959
 
7.5%
o 89178
 
5.6%
n 79385
 
5.0%
A 74168
 
4.7%
i 73578
 
4.6%
s 73038
 
4.6%
Other values (16) 470950
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1179204
74.1%
Uppercase Letter 269802
 
17.0%
Space Separator 141738
 
8.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 185242
15.7%
r 153725
13.0%
a 130783
11.1%
t 118959
10.1%
o 89178
7.6%
n 79385
6.7%
i 73578
 
6.2%
s 73038
 
6.2%
u 66721
 
5.7%
c 46099
 
3.9%
Other values (7) 162496
13.8%
Uppercase Letter
ValueCountFrequency (%)
A 74168
27.5%
S 50419
18.7%
E 48020
17.8%
C 32512
12.1%
W 30999
11.5%
U 17751
 
6.6%
N 8917
 
3.3%
O 7016
 
2.6%
Space Separator
ValueCountFrequency (%)
141738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1449006
91.1%
Common 141738
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 185242
12.8%
r 153725
 
10.6%
a 130783
 
9.0%
t 118959
 
8.2%
o 89178
 
6.2%
n 79385
 
5.5%
A 74168
 
5.1%
i 73578
 
5.1%
s 73038
 
5.0%
u 66721
 
4.6%
Other values (15) 404229
27.9%
Common
ValueCountFrequency (%)
141738
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1590744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 185242
 
11.6%
r 153725
 
9.7%
141738
 
8.9%
a 130783
 
8.2%
t 118959
 
7.5%
o 89178
 
5.6%
n 79385
 
5.0%
A 74168
 
4.7%
i 73578
 
4.6%
s 73038
 
4.6%
Other values (16) 470950
29.6%
Distinct1081
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:19.974351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length36
Median length31
Mean length10.81629
Min length3

Characters and Unicode

Total characters1363707
Distinct characters83
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowUttar Pradesh
2nd rowAustralia del Sur
3rd rowEstambul
4th rowUlán Bator
5th rowEstambul
ValueCountFrequency (%)
de 6255
 
3.4%
del 5715
 
3.1%
inglaterra 4494
 
2.4%
california 3935
 
2.1%
nueva 3799
 
2.1%
isla 3173
 
1.7%
francia 3109
 
1.7%
san 2776
 
1.5%
sur 2299
 
1.3%
renania 2195
 
1.2%
Other values (1163) 146117
79.5%
2024-09-19T15:21:20.252919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 215433
15.8%
n 99116
 
7.3%
i 91568
 
6.7%
e 90536
 
6.6%
r 81327
 
6.0%
o 80150
 
5.9%
l 71501
 
5.2%
57788
 
4.2%
t 55573
 
4.1%
s 52662
 
3.9%
Other values (73) 468053
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1101198
80.8%
Uppercase Letter 186721
 
13.7%
Space Separator 57788
 
4.2%
Dash Punctuation 16592
 
1.2%
Other Punctuation 470
 
< 0.1%
Close Punctuation 469
 
< 0.1%
Open Punctuation 469
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 215433
19.6%
n 99116
9.0%
i 91568
8.3%
e 90536
8.2%
r 81327
 
7.4%
o 80150
 
7.3%
l 71501
 
6.5%
t 55573
 
5.0%
s 52662
 
4.8%
u 43549
 
4.0%
Other values (35) 219783
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 21157
 
11.3%
S 19091
 
10.2%
A 14257
 
7.6%
P 12919
 
6.9%
M 11661
 
6.2%
N 10990
 
5.9%
I 9675
 
5.2%
B 8774
 
4.7%
G 8680
 
4.6%
L 7851
 
4.2%
Other values (22) 61666
33.0%
Other Punctuation
ValueCountFrequency (%)
? 377
80.2%
' 93
 
19.8%
Space Separator
ValueCountFrequency (%)
57788
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16592
100.0%
Close Punctuation
ValueCountFrequency (%)
) 469
100.0%
Open Punctuation
ValueCountFrequency (%)
( 469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1287919
94.4%
Common 75788
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 215433
16.7%
n 99116
 
7.7%
i 91568
 
7.1%
e 90536
 
7.0%
r 81327
 
6.3%
o 80150
 
6.2%
l 71501
 
5.6%
t 55573
 
4.3%
s 52662
 
4.1%
u 43549
 
3.4%
Other values (67) 406504
31.6%
Common
ValueCountFrequency (%)
57788
76.2%
- 16592
 
21.9%
) 469
 
0.6%
( 469
 
0.6%
? 377
 
0.5%
' 93
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1337585
98.1%
None 26122
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 215433
16.1%
n 99116
 
7.4%
i 91568
 
6.8%
e 90536
 
6.8%
r 81327
 
6.1%
o 80150
 
6.0%
l 71501
 
5.3%
57788
 
4.3%
t 55573
 
4.2%
s 52662
 
3.9%
Other values (48) 441931
33.0%
None
ValueCountFrequency (%)
í 8248
31.6%
á 7097
27.2%
ó 3196
 
12.2%
é 2372
 
9.1%
ñ 2159
 
8.3%
ã 1476
 
5.7%
ú 632
 
2.4%
ü 256
 
1.0%
à 124
 
0.5%
ô 122
 
0.5%
Other values (15) 440
 
1.7%

Order Status
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
COMPLETE
41586 
PENDING_PAYMENT
27805 
PROCESSING
15196 
PENDING
14151 
CLOSED
13651 
Other values (4)
13690 

Length

Max length15
Median length14
Mean length9.6215309
Min length6

Characters and Unicode

Total characters1213073
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPENDING_PAYMENT
2nd rowPENDING_PAYMENT
3rd rowPENDING_PAYMENT
4th rowPENDING_PAYMENT
5th rowPENDING_PAYMENT

Common Values

ValueCountFrequency (%)
COMPLETE 41586
33.0%
PENDING_PAYMENT 27805
22.1%
PROCESSING 15196
 
12.1%
PENDING 14151
 
11.2%
CLOSED 13651
 
10.8%
ON_HOLD 6927
 
5.5%
SUSPECTED_FRAUD 2834
 
2.2%
CANCELED 2603
 
2.1%
PAYMENT_REVIEW 1326
 
1.1%

Length

2024-09-19T15:21:20.359949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:20.478977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
complete 41586
33.0%
pending_payment 27805
22.1%
processing 15196
 
12.1%
pending 14151
 
11.2%
closed 13651
 
10.8%
on_hold 6927
 
5.5%
suspected_fraud 2834
 
2.2%
canceled 2603
 
2.1%
payment_review 1326
 
1.1%

Most occurring characters

ValueCountFrequency (%)
E 196632
16.2%
N 137769
11.4%
P 130703
10.8%
O 84287
 
6.9%
C 78473
 
6.5%
T 73551
 
6.1%
D 70805
 
5.8%
M 70717
 
5.8%
L 64767
 
5.3%
I 58478
 
4.8%
Other values (11) 246891
20.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1174181
96.8%
Connector Punctuation 38892
 
3.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 196632
16.7%
N 137769
11.7%
P 130703
11.1%
O 84287
7.2%
C 78473
 
6.7%
T 73551
 
6.3%
D 70805
 
6.0%
M 70717
 
6.0%
L 64767
 
5.5%
I 58478
 
5.0%
Other values (10) 207999
17.7%
Connector Punctuation
ValueCountFrequency (%)
_ 38892
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1174181
96.8%
Common 38892
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 196632
16.7%
N 137769
11.7%
P 130703
11.1%
O 84287
7.2%
C 78473
 
6.7%
T 73551
 
6.3%
D 70805
 
6.0%
M 70717
 
6.0%
L 64767
 
5.5%
I 58478
 
5.0%
Other values (10) 207999
17.7%
Common
ValueCountFrequency (%)
_ 38892
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1213073
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 196632
16.2%
N 137769
11.4%
P 130703
10.8%
O 84287
 
6.9%
C 78473
 
6.5%
T 73551
 
6.1%
D 70805
 
5.8%
M 70717
 
5.8%
L 64767
 
5.3%
I 58478
 
4.8%
Other values (11) 246891
20.4%
Distinct94
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:20.727264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length45
Median length43
Mean length34.123557
Min length5

Characters and Unicode

Total characters4302264
Distinct characters64
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPerfect Fitness Perfect Rip Deck
2nd rowNike Men's Dri-FIT Victory Golf Polo
3rd rowUnder Armour Girls' Toddler Spine Surge Runni
4th rowNike Men's Dri-FIT Victory Golf Polo
5th rowNike Men's Dri-FIT Victory Golf Polo
ValueCountFrequency (%)
men's 65544
 
9.0%
nike 47100
 
6.5%
perfect 42930
 
5.9%
golf 23969
 
3.3%
fitness 21465
 
3.0%
deck 21465
 
3.0%
rip 21465
 
3.0%
cleat 20779
 
2.9%
2 20577
 
2.8%
elite 20321
 
2.8%
Other values (284) 419711
57.9%
2024-09-19T15:21:21.554003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
600607
 
14.0%
e 518357
 
12.0%
i 259918
 
6.0%
r 226506
 
5.3%
n 226060
 
5.3%
t 199120
 
4.6%
o 193827
 
4.5%
l 179276
 
4.2%
s 175689
 
4.1%
a 118913
 
2.8%
Other values (54) 1603991
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2683859
62.4%
Uppercase Letter 805096
 
18.7%
Space Separator 600607
 
14.0%
Other Punctuation 104578
 
2.4%
Decimal Number 81113
 
1.9%
Dash Punctuation 20958
 
0.5%
Math Symbol 6053
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 81844
 
10.2%
F 71327
 
8.9%
M 68656
 
8.5%
N 67135
 
8.3%
D 64467
 
8.0%
T 58194
 
7.2%
S 49470
 
6.1%
C 44845
 
5.6%
G 41417
 
5.1%
R 39244
 
4.9%
Other values (15) 218497
27.1%
Lowercase Letter
ValueCountFrequency (%)
e 518357
19.3%
i 259918
9.7%
r 226506
8.4%
n 226060
8.4%
t 199120
 
7.4%
o 193827
 
7.2%
l 179276
 
6.7%
s 175689
 
6.5%
a 118913
 
4.4%
c 105602
 
3.9%
Other values (14) 480591
17.9%
Decimal Number
ValueCountFrequency (%)
0 35134
43.3%
2 21070
26.0%
1 15737
19.4%
5 6485
 
8.0%
6 1139
 
1.4%
4 535
 
0.7%
8 516
 
0.6%
3 497
 
0.6%
Other Punctuation
ValueCountFrequency (%)
' 97542
93.3%
. 6744
 
6.4%
/ 261
 
0.2%
& 31
 
< 0.1%
Space Separator
ValueCountFrequency (%)
600607
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20958
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3488955
81.1%
Common 813309
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 518357
14.9%
i 259918
 
7.4%
r 226506
 
6.5%
n 226060
 
6.5%
t 199120
 
5.7%
o 193827
 
5.6%
l 179276
 
5.1%
s 175689
 
5.0%
a 118913
 
3.4%
c 105602
 
3.0%
Other values (39) 1285687
36.9%
Common
ValueCountFrequency (%)
600607
73.8%
' 97542
 
12.0%
0 35134
 
4.3%
2 21070
 
2.6%
- 20958
 
2.6%
1 15737
 
1.9%
. 6744
 
0.8%
5 6485
 
0.8%
+ 6053
 
0.7%
6 1139
 
0.1%
Other values (5) 1840
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4302264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
600607
 
14.0%
e 518357
 
12.0%
i 259918
 
6.0%
r 226506
 
5.3%
n 226060
 
5.3%
t 199120
 
4.6%
o 193827
 
4.5%
l 179276
 
4.2%
s 175689
 
4.1%
a 118913
 
2.8%
Other values (54) 1603991
37.3%

Product Price
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.329405
Minimum9.9899998
Maximum249.99001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-09-19T15:21:21.676031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.9899998
5-th percentile25
Q149.98
median59.990002
Q3129.99001
95-th percentile199.99001
Maximum249.99001
Range240.00001
Interquartile range (IQR)80.010006

Descriptive statistics

Standard deviation54.526666
Coefficient of variation (CV)0.65435084
Kurtosis-0.019828239
Mean83.329405
Median Absolute Deviation (MAD)20
Skewness1.1008913
Sum10506088
Variance2973.1573
MonotonicityNot monotonic
2024-09-19T15:21:21.802565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.99000168 21733
17.2%
129.9900055 20385
16.2%
50 19114
15.2%
49.97999954 17483
13.9%
199.9900055 14084
11.2%
39.99000168 10366
8.2%
99.98999786 6186
 
4.9%
24.98999977 2231
 
1.8%
31.98999977 1373
 
1.1%
30 861
 
0.7%
Other values (47) 12263
9.7%
ValueCountFrequency (%)
9.989999771 280
 
0.2%
11.28999996 271
 
0.2%
11.53999996 529
 
0.4%
14.98999977 576
 
0.5%
15.98999977 583
 
0.5%
17.98999977 293
 
0.2%
19.98999977 859
 
0.7%
21.98999977 283
 
0.2%
22 297
 
0.2%
24.98999977 2231
1.8%
ValueCountFrequency (%)
249.9900055 110
 
0.1%
215.8200073 598
 
0.5%
210.8500061 183
 
0.1%
209.9900055 56
 
< 0.1%
199.9900055 14084
11.2%
199 61
 
< 0.1%
189 57
 
< 0.1%
179.9900055 57
 
< 0.1%
169.9900055 59
 
< 0.1%
164.3800049 432
 
0.3%

Shipping Mode
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Standard Class
75188 
Second Class
24646 
First Class
19373 
Same Day
 
6872

Length

Max length14
Median length14
Mean length12.821033
Min length8

Characters and Unicode

Total characters1616463
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond Class
2nd rowSecond Class
3rd rowSecond Class
4th rowSecond Class
5th rowSecond Class

Common Values

ValueCountFrequency (%)
Standard Class 75188
59.6%
Second Class 24646
 
19.5%
First Class 19373
 
15.4%
Same Day 6872
 
5.5%

Length

2024-09-19T15:21:21.919446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T15:21:22.036475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
class 119207
47.3%
standard 75188
29.8%
second 24646
 
9.8%
first 19373
 
7.7%
same 6872
 
2.7%
day 6872
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 283327
17.5%
s 257787
15.9%
d 175022
10.8%
126079
7.8%
l 119207
7.4%
C 119207
7.4%
S 106706
 
6.6%
n 99834
 
6.2%
r 94561
 
5.8%
t 94561
 
5.8%
Other values (8) 140172
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1238226
76.6%
Uppercase Letter 252158
 
15.6%
Space Separator 126079
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 283327
22.9%
s 257787
20.8%
d 175022
14.1%
l 119207
9.6%
n 99834
 
8.1%
r 94561
 
7.6%
t 94561
 
7.6%
e 31518
 
2.5%
c 24646
 
2.0%
o 24646
 
2.0%
Other values (3) 33117
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 119207
47.3%
S 106706
42.3%
F 19373
 
7.7%
D 6872
 
2.7%
Space Separator
ValueCountFrequency (%)
126079
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1490384
92.2%
Common 126079
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 283327
19.0%
s 257787
17.3%
d 175022
11.7%
l 119207
8.0%
C 119207
8.0%
S 106706
 
7.2%
n 99834
 
6.7%
r 94561
 
6.3%
t 94561
 
6.3%
e 31518
 
2.1%
Other values (7) 108654
 
7.3%
Common
ValueCountFrequency (%)
126079
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1616463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 283327
17.5%
s 257787
15.9%
d 175022
10.8%
126079
7.8%
l 119207
7.4%
C 119207
7.4%
S 106706
 
6.6%
n 99834
 
6.2%
r 94561
 
5.8%
t 94561
 
5.8%
Other values (8) 140172
8.7%

Interactions

2024-09-19T15:21:11.749065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:00.868860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.955136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.028072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.063318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.071547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.164795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.271044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.294278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:10.327636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:11.854087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:00.985886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:02.065162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.134096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.162340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.176571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.323830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.379069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.400922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:10.439942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:11.962112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.094912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:02.178187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.241121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.265364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.284595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.441857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.486093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.508946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:10.552968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:12.064135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.199948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:03.286903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.347145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.367387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.392619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.549408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.587116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.610969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:10.659992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:12.158156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.300473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:03.386927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.444166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.459408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.487641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.648430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.683137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.708991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:11.099596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:12.262181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.405001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:03.494951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.549190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.564431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.593664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.755470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.786161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.813015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:11.206939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:12.358202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.506024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:03.599975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.650213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.661454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.693687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.860448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.888184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.917038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:11.312962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:12.464226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.608551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:03.706000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.751236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.762476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.796710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.961974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.988207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:10.016565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:11.423989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:12.566931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.717082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:03.816024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.856260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.863499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:06.902735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.062997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.091232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:10.118588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:11.529015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:12.675948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:01.827107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:03.925049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:04.965285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:05.969523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:07.011759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:08.173022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:09.196256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:10.227613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-19T15:21:11.641040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-09-19T15:21:22.151674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Benefit per orderCategory NameCustomer CountryCustomer IdCustomer SegmentCustomer StateDays for shipment (scheduled)Days for shipping (real)Delivery StatusDepartment NameLate_delivery_riskMarketOrder Item DiscountOrder Item Product PriceOrder Item QuantityOrder Item TotalOrder Profit Per OrderOrder RegionOrder StatusProduct PriceSalesSales per customerShipping ModeType
Benefit per order1.0000.1510.000-0.0300.0000.0010.001-0.0000.0000.0950.0010.0210.1990.2180.2200.5211.0000.0140.0020.2180.5130.5210.0010.002
Category Name0.1511.0000.0000.5050.0110.0050.0150.0050.0000.9900.0020.1520.1370.8730.3670.4040.1510.0730.0000.8730.5080.4040.0150.000
Customer Country0.0000.0001.0000.0130.0161.0000.0100.0100.0040.0000.0000.0130.0030.0000.0000.0050.0000.0160.0080.0000.0060.0050.0100.007
Customer Id-0.0300.5050.0131.0000.0330.0500.013-0.0000.0070.3600.0000.154-0.028-0.0150.095-0.044-0.0300.1160.010-0.015-0.043-0.0440.0130.004
Customer Segment0.0000.0110.0160.0331.0000.0620.0080.0110.0050.0050.0000.0020.0000.0030.0050.0070.0000.0180.0100.0030.0070.0070.0080.004
Customer State0.0010.0051.0000.0500.0621.0000.0310.0280.0260.0100.0180.0310.0000.0000.0000.0050.0010.0260.0230.0000.0000.0050.0310.023
Days for shipment (scheduled)0.0010.0150.0100.0130.0080.0311.0000.6810.3210.0110.4570.0060.0000.0000.0030.0000.0010.0230.0070.0000.0000.0001.0000.006
Days for shipping (real)-0.0000.0050.010-0.0000.0110.0280.6811.0000.5610.0080.6320.0070.001-0.0020.000-0.001-0.0000.0220.008-0.002-0.001-0.0010.6810.010
Delivery Status0.0000.0000.0040.0070.0050.0260.3210.5611.0000.0041.0000.0070.0000.0000.0000.0020.0000.0180.5770.0000.0000.0020.3210.199
Department Name0.0950.9900.0000.3600.0050.0100.0110.0080.0041.0000.0020.1010.0920.5790.1990.2860.0950.0820.0000.5790.3610.2860.0110.000
Late_delivery_risk0.0010.0020.0000.0000.0000.0180.4570.6321.0000.0021.0000.0060.0050.0040.0000.0060.0010.0160.2340.0040.0000.0060.4570.080
Market0.0210.1520.0130.1540.0020.0310.0060.0070.0070.1010.0061.0000.0140.0860.0240.0490.0211.0000.0110.0860.0540.0490.0060.012
Order Item Discount0.1990.1370.003-0.0280.0000.0000.0000.0010.0000.0920.0050.0141.0000.2170.1890.3260.1990.0110.0000.2170.4780.3260.0000.000
Order Item Product Price0.2180.8730.000-0.0150.0030.0000.000-0.0020.0000.5790.0040.0860.2171.0000.3560.3700.2180.0640.0041.0000.4020.3700.0000.000
Order Item Quantity0.2200.3670.0000.0950.0050.0000.0030.0000.0000.1990.0000.0240.1890.3561.0000.4910.2200.0280.0000.3560.5930.4910.0030.000
Order Item Total0.5210.4040.005-0.0440.0070.0050.000-0.0010.0020.2860.0060.0490.3260.3700.4911.0000.5210.0360.0050.3700.9781.0000.0000.003
Order Profit Per Order1.0000.1510.000-0.0300.0000.0010.001-0.0000.0000.0950.0010.0210.1990.2180.2200.5211.0000.0140.0020.2180.5130.5210.0010.002
Order Region0.0140.0730.0160.1160.0180.0260.0230.0220.0180.0820.0161.0000.0110.0640.0280.0360.0141.0000.0180.0640.0400.0360.0230.021
Order Status0.0020.0000.0080.0100.0100.0230.0070.0080.5770.0000.2340.0110.0000.0040.0000.0050.0020.0181.0000.0040.0020.0050.0071.000
Product Price0.2180.8730.000-0.0150.0030.0000.000-0.0020.0000.5790.0040.0860.2171.0000.3560.3700.2180.0640.0041.0000.4020.3700.0000.000
Sales0.5130.5080.006-0.0430.0070.0000.000-0.0010.0000.3610.0000.0540.4780.4020.5930.9780.5130.0400.0020.4021.0000.9780.0000.002
Sales per customer0.5210.4040.005-0.0440.0070.0050.000-0.0010.0020.2860.0060.0490.3260.3700.4911.0000.5210.0360.0050.3700.9781.0000.0000.003
Shipping Mode0.0010.0150.0100.0130.0080.0311.0000.6810.3210.0110.4570.0060.0000.0000.0030.0000.0010.0230.0070.0000.0000.0001.0000.006
Type0.0020.0000.0070.0040.0040.0230.0060.0100.1990.0000.0800.0120.0000.0000.0000.0030.0020.0211.0000.0000.0020.0030.0061.000

Missing values

2024-09-19T15:21:12.884995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-19T15:21:13.408543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TypeDays for shipping (real)Days for shipment (scheduled)Benefit per orderSales per customerDelivery StatusLate_delivery_riskCategory NameCustomer CityCustomer CountryCustomer IdCustomer SegmentCustomer StateDepartment NameMarketOrder CityOrder CountryOrder Item DiscountOrder Item Product PriceOrder Item QuantitySalesOrder Item TotalOrder Profit Per OrderOrder RegionOrder StateOrder StatusProduct NameProduct PriceShipping Mode
48PAYMENT52-30.750000115.180000Late delivery1CleatsBayamonPuerto Rico9083Home OfficePRApparelPacific AsiaMirzapurIndia4.80000059.9900022119.980003115.180000-30.750000South AsiaUttar PradeshPENDING_PAYMENTPerfect Fitness Perfect Rip Deck59.990002Second Class
50PAYMENT6233.59999896.000000Late delivery1Women's ApparelCaguasPuerto Rico639Home OfficePRGolfPacific AsiaMurray BridgeAustralia4.00000050.0000002100.00000096.00000033.599998OceaniaAustralia del SurPENDING_PAYMENTNike Men's Dri-FIT Victory Golf Polo50.000000Second Class
51PAYMENT2224.69000175.980003Shipping on time0Shop By SportCaguasPuerto Rico9702Home OfficePRGolfPacific AsiaKartalTurquía4.00000039.990002279.98000375.98000324.690001West AsiaEstambulPENDING_PAYMENTUnder Armour Girls' Toddler Spine Surge Runni39.990002Second Class
52PAYMENT329.10000091.000000Late delivery1Women's ApparelCaguasPuerto Rico9114Home OfficePRGolfPacific AsiaUlan BatorMongolia9.00000050.0000002100.00000091.0000009.100000Eastern AsiaUlán BatorPENDING_PAYMENTNike Men's Dri-FIT Victory Golf Polo50.000000Second Class
53PAYMENT52-21.75000087.000000Late delivery1Women's ApparelCaguasPuerto Rico1362Home OfficePRGolfPacific AsiaEstambulTurquía13.00000050.0000002100.00000087.000000-21.750000West AsiaEstambulPENDING_PAYMENTNike Men's Dri-FIT Victory Golf Polo50.000000Second Class
54PAYMENT626.15000082.000000Late delivery1Women's ApparelCaguasPuerto Rico8011Home OfficePRGolfPacific AsiaRaipurIndia18.00000050.0000002100.00000082.0000006.150000South AsiaRajastánPENDING_PAYMENTNike Men's Dri-FIT Victory Golf Polo50.000000Second Class
55PAYMENT2222.41000074.680000Shipping on time0ElectronicsCaguasPuerto Rico3296Home OfficePRFootwearUSCAPascoEstados Unidos15.30000044.990002289.98000374.68000022.410000West of USAWashingtonPENDING_PAYMENTUnder Armour Men's Compression EV SL Slide44.990002Second Class
56PAYMENT5225.24000090.150002Late delivery1Boxing & MMACaguasPuerto Rico3182Home OfficePRFootwearUSCALos AngelesEstados Unidos19.79000154.9700012109.94000290.15000225.240000West of USACaliforniaPENDING_PAYMENTUnder Armour Women's Micro G Skulpt Running S54.970001Second Class
57PAYMENT6230.570000117.580002Late delivery1CleatsCaguasPuerto Rico7864Home OfficePRApparelUSCASan FranciscoEstados Unidos2.40000059.9900022119.980003117.58000230.570000West of USACaliforniaPENDING_PAYMENTPerfect Fitness Perfect Rip Deck59.990002Second Class
58PAYMENT4246.07000095.980003Late delivery1CleatsCaguasPuerto Rico11169Home OfficePRApparelUSCAOverland ParkEstados Unidos24.00000059.9900022119.98000395.98000346.070000US CenterKansasPENDING_PAYMENTPerfect Fitness Perfect Rip Deck59.990002Second Class
TypeDays for shipping (real)Days for shipment (scheduled)Benefit per orderSales per customerDelivery StatusLate_delivery_riskCategory NameCustomer CityCustomer CountryCustomer IdCustomer SegmentCustomer StateDepartment NameMarketOrder CityOrder CountryOrder Item DiscountOrder Item Product PriceOrder Item QuantitySalesOrder Item TotalOrder Profit Per OrderOrder RegionOrder StateOrder StatusProduct NameProduct PriceShipping Mode
179619TRANSFER2472.889999269.959992Advance shipping0CleatsCaguasPuerto Rico11659ConsumerPRApparelLATAMTegucigalpaHonduras30.00000059.9900025299.950012269.95999272.889999Central AmericaFrancisco MorazánPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179620TRANSFER54-53.990002269.959992Late delivery1CleatsCaguasPuerto Rico5907ConsumerPRApparelLATAMMasayaNicaragua30.00000059.9900025299.950012269.959992-53.990002Central AmericaMasayaPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179621TRANSFER34-26.100000260.959992Advance shipping0CleatsJuana DiazPuerto Rico10344ConsumerPRApparelLATAMVillahermosaMéxico38.99000259.9900025299.950012260.959992-26.100000Central AmericaTabascoPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179622TRANSFER4481.680000260.959992Shipping on time0CleatsCaguasPuerto Rico8386ConsumerPRApparelLATAMManaguaNicaragua38.99000259.9900025299.950012260.95999281.680000Central AmericaManaguaPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179623TRANSFER24117.430000260.959992Advance shipping0CleatsCaguasPuerto Rico8917ConsumerPRApparelLATAMBarueriBrasil38.99000259.9900025299.950012260.959992117.430000South AmericaSão PauloPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179624TRANSFER3448.439999254.960007Advance shipping0CleatsCaguasPuerto Rico9204ConsumerPRApparelLATAMMaceióBrasil44.99000259.9900025299.950012254.96000748.439999South AmericaAlagoasPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179625TRANSFER6441.070000251.960007Late delivery1CleatsCaguasPuerto Rico7190ConsumerPRApparelLATAMSanta AnaEl Salvador47.99000259.9900025299.950012251.96000741.070000Central AmericaSanta AnaPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179626TRANSFER3492.110001248.960007Advance shipping0CleatsCaguasPuerto Rico10240ConsumerPRApparelLATAMCholutecaHonduras50.99000259.9900025299.950012248.96000792.110001Central AmericaCholutecaPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179627TRANSFER4476.989998245.960007Shipping on time0CleatsCaguasPuerto Rico3ConsumerPRApparelLATAMTegucigalpaHonduras53.99000259.9900025299.950012245.96000776.989998Central AmericaFrancisco MorazánPENDINGPerfect Fitness Perfect Rip Deck59.990002Standard Class
179629TRANSFER4437.590000199.949997Shipping on time0Shop By SportCaguasPuerto Rico482ConsumerPRGolfLATAMLa RomanaRepública Dominicana0.00000039.9900025199.949997199.94999737.590000CaribbeanLa RomanaPENDINGUnder Armour Girls' Toddler Spine Surge Runni39.990002Standard Class